Report

Skills Mismatch Measurement – Egypt, Georgia, Moldova, Montenegro, Morocco, North Macedonia and Serbia

Matching skills supply and demand is a major challenge for many countries around the world. In 2017‒18 the ETF carried out a project on skills mismatch measurement in seven countries – Egypt, Georgia, Moldova, Montenegro, Morocco, North Macedonia and Serbia – to better understand the nature and incidence of this complex phenomenon. Based on the findings of the country analyses, this report describes and interprets a series of indicators and the way they are interrelated, and provides information on the methodology and data sources used to measure skills mismatch.


This report focuses on a critical concern for the ETF’s partner countries and other countries around the world. Skills mismatch is recognised as a major challenge by policy makers, practitioners and social partners, as it is often associated with dynamic social and economic contexts such as restructuring processes, changing trade patterns, technological progress, demographic change or negative social aspects (e.g. informality, long-term unemployment, inactivity).

Skills mismatch is a complex phenomenon, expressed in different types and dimensions of labour market friction. A combination of indicators and analyses of results from different methods is required to measure and understand the magnitude and interrelatedness of the different forms of skills mismatch. However, the data sources needed to measure and predict the different forms of skills mismatch are not always readily available in all ETF partner countries, and only a few international studies have included ETF partner countries. An expanded set of indicators needs to be calculated and analysed from multiple angles. The aim of the project on which this report is based was to assess the suitability of selected skills mismatch indicators for practical implementation in ETF partner countries.
In 2017, the ETF launched a project on skills mismatch measurement in the ETF partner countries. Its objective was twofold: to identify available data sources and to test a series of indicators capable of capturing various angles and implications of skills mismatches. This project built on previous conceptual work conducted by the ETF on skills mismatch measurement and applied research carried out in 2011 (ETF, 2012).

Using a combination of international and local expertise, and in consultation with national stakeholders, the ETF project aimed to review the suitability of the indicators and methods for measuring the incidence of mismatch. This included testing a methodological approach that was adapted to the context of selected countries (transition or developing countries), while ensuring, as much as possible, comparability across ETF partner countries and with European or international research on similar topics (e.g. Cedefop, OECD, ILO).

Seven ETF partner countries were included in the two phases of the project (2017 and 2018): Egypt, Georgia, Moldova, Montenegro, Morocco, North Macedonia and Serbia. Country-specific analyses were developed to contextualise the skills mismatch measurement for each country and to analyse the insights gained from each of the indicators that were calculated.

This report complements the country analyses and findings and highlights commonalities across countries. Moreover, the report delves into methodological aspects with a view to possibly replicating the skills mismatch measurement and analysis in other ETF partner countries, and embedding this analytical capacity in national policy development.

The usefulness of the term ‘skills mismatch’ has been criticised by the ILO (2017) as an overly generic umbrella term that hides different forms of mismatch, each with different manifestations, requiring different measurement methods and different policy responses. The term is used in relation to vertical and horizontal mismatch, skills gaps, skills shortages, and skills obsolescence. Some forms of skills mismatch have been the subject of extensive research initiatives, as demonstrated by the large number of published analyses on vertical mismatch (over-education, under-education, and to a lesser extent, also over-skilling). Other forms of mismatch, such as horizontal mismatch, skills shortages, skills gaps and skills obsolescence are less represented in the literature. Some concepts of skills mismatch have drawbacks and some measures are poorly correlated.

The available data and the nature of the indicators used have both strengths and weaknesses. Skills mismatch is mostly measured by proxy in this ETF project, with data on education and occupation used as proxies of skills. Moreover, the nature of the methodologies and indicators used determines the relative limitations of their predictions. For example, the proportion of unemployed people versus employed people indicates the direction of the mismatch (i.e. the deficit or surplus of specific education levels) and generalises at the macro level. Other indicators, such as the coefficient of variation and the variance of relative rates, show the magnitude of mismatch and generalise at the macro level. The Beveridge curve measures the relationship between unemployment and vacancy rates, but might be limited for most ETF partner countries.

A deeper knowledge of the nature and incidence of skills mismatch, including good contextualisation (e.g. socio-economic aspects, labour regulations, job matching services), can help countries to better target their efforts to match supply and demand.
This can be done through a wider set of policies and measures covering education, training, employment and other policy interventions directed at better utilisation of skills and labour resources. Such an analytical exercise may also help institutions and partners to assess the effectiveness of their skills policies.

In describing and interpreting the indicators and, where possible also how they are interrelated, we provide information about the methodology and data sources used to measure skills mismatch.

We also hope to help clarify the incidence of skills mismatch and provide some predictive insights into areas where mismatches might occur in the labour market. Anyone who generates, interprets or uses labour market information or is involved in labour market and/or education policy may be interested in understanding the various ways in which the labour market and skills can be analysed. Finally, proposals on how to further develop indicators, data infrastructure or skills and labour market analysis are provided for the various countries and the ETF.

Chapter 1 introduces the background to this initiative, the methodological anchors and previous work done by the ETF on the subject. Chapter 2 delves into general methodological insights on the definition, measurement and interpretation of skills mismatches. Chapter 3 includes the methods chosen and the steps implemented in the ETF project to collect and prepare the datasets for selected countries. The reasoning behind selecting a number of key mismatch indicators is also discussed in this chapter, as one of the main principles guiding the research was to ensure, as far as possible, comparability of calculations and findings across countries. Using a cross-country comparative analysis, Chapter 4 focuses on the actual findings of the mismatch measurement in the selected partner countries. The findings include calculation results, interpretation, possible caveats and data limitations. The final chapter discusses the lessons learned in the implementation of the methodology using national data, draws conclusions and recommends possible avenues for the partner countries and the ETF to replicate and further analyse the incidence and nature of skills mismatches.

Chosen excerpts by Job Market Monitor. Read the whole story at Skills mismatch measurement in ETF partner countries | ETF

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